In the field of human-computer interaction technologies, a human face provides a lot of very valuable information such as skin color, profile, and expression. Especially, in recent years, positioning technologies of feature points of a human face have rapidly developed, and are widely applied in various technical fields such as identity recognition, human face three-dimensional reconstruction, human face beautification, posture estimation, and human face tracking. The positioning technology of feature points of a human face refers to detecting a human face based on a detection technology of a human face, and performing precise calculation on position information and shape information of feature points of the detected human face. Precise positioning of the feature points of human face edge determines the shape of the human face.
Currently disclosed methods for positioning feature points of a human face are mainly methods for positioning feature points of a human face based on ASM (Active Shape Model), AAM (Active Appearance Model), and SDM (Supervised Descent Method) algorithms. The ASM algorithm positions feature points of a human face based on a shape statistic model. For example, a shape such as a human face profile may be expressed by sequentially connecting coordinates of several key feature points in series to form a shape vector. The AAM algorithm further performs, on the basis of the ASM algorithm, statistical modeling on texture (a shape-unrelated image obtained by deforming a human face image into an average shape), and merges the two statistical models, namely, the shape statistical model and the texture statistical model, into an appearance model. The SDM algorithm extracts, according to initial positions of the feature points of a human face, each non-linear SIFT (Scale Invariant Feature Transform) feature using a feature point as the center, and then solves an NLS (Non-linear Least Squares) problem between the feature point and the corresponding SIFT feature, thereby implementing positioning of the feature points of a human face.
The methods for positioning feature points of a human face provided in the above conventional techniques have apparent defects.
The methods for positioning feature points of a human face provided in the conventional techniques have the following defects: in the above method for positioning feature points of a human face based on the SDM algorithm, in the process of positioning the feature points of a human face, the feature points of the human face need to be extracted for converging iteration. However, when the human face in a human face image is in different backgrounds, it is very difficult to extract the feature points of the human face, especially when the human face in a human face image is in a complicated background, it is difficult to determine accurate positions of the feature points of the human face, and therefore, the accuracy of the feature points of the human face determined in this method is low.